ReLU (Rectified Linear Unit) is one of the most widely used activation functions in neural networks, particularly in deep learning models. Formula: ReLU(x) = max(0, x) This can also be written as: ReLU(x) = x if x > 0 ReLU(x) = 0 if x ≤ 0 How it works: ReLU simply outputs the input value if it's positive, and outputs zero if the input is negative or zero. This creates a "rectified" linear function that eliminates negative values while preserving positive ones unchanged. Key characteristics: Simplicity : The function is computationally efficient, involving only a simple comparison and selection Non-linearity : Despite its simple form, ReLU introduces non-linearity into the network, which is essential for learning complex patterns Sparsity : Many neurons will output zero, creating sparse representations that can be computationally beneficial No saturation : Unlike sigmoid or tanh functions, ReLU doesn't saturate for positive values, helping to mitigate the vanishing gradient problem Advantages: Fast computation (just a max operation) Helps reduce vanishing gradient problems Promotes sparsity in neural networks Works well in practice for many applications Disadvantages: Can suffer from the "dying ReLU" problem where neurons get stuck outputting zero Not differentiable at x = 0 (though this is typically handled by setting the derivative to 0 or 1 at that point)

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Describe Relu activation

Saurabh Prajapati

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What is relu

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